PyGo: A Deep Learning Framework Where Go Calls Python Calls C++

[Project] PyGo – embedding CPython inside a Go process to build a deep learning framework I've been working on something a bit unusual: a deep learning framework where Go is the top-level API, Python handles autograd and the model zoo, and C++/CUDA does the raw compute. The architecture looks like this: Go API → CGo bridge → CPython (embedded) → pybind11 → CUDA/AVX-512 kernels Enter fullscreen mode Exit fullscreen mode The key insight: instead of a Python sidecar in every pod, CPython runs inside the Go binary. Tensors live in shared memory — zero-copy across all three layers. Why Go on top? Go is already running most ML infrastructure (K8s, Prometheus, etcd). PyGo makes models first-class citizens there, without rewriting your ops stack. Goroutines make dynamic batching trivial. Current state: LLaMA-3, GPT-2, BERT, ViT, Whisper partially implemented Flash Attention v2, GPTQ/AWQ quantisation FSDP, DPO, SFT trainers Early stage — looking for Go + C++/CUDA contributors The main unsolved problem is CGo call overhead at the tensor boundary. If anyone has experience embedding CPython in a Go process, I'd love to talk. Looking for core contributors — especially Go devs with CGo experience, Python autograd engineers, and C++/CUDA kernel writers. Interested in joining? Email: amitsarkar1066@gmail.com